Corporate ESG Profiles and Investor Horizons: Starks, Venkat & Zhu (2026)

What this is. The paper’s core results, the hypotheses it tests, and the regression specifications behind each finding: enough to know what it found and how, without reading all 40 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using a large sample of US mutual funds and 13f institutional investors from 2000 to 2018, the paper documents that longer-horizon investors (lower portfolio turnover or churn ratios) tilt their portfolios toward firms with higher ESG scores, and that firms with better ESG profiles attract shareholder bases with longer investment horizons. Three mechanisms are examined: (i) an information channel, where long-term investors specialize in analyzing long-payoff ESG signals; (ii) a limits-to-arbitrage channel, where lower flow-performance sensitivity enables long-term investors to hold illiquid ESG positions; and (iii) a clientele-catering channel. Evidence supports the first two channels. A 2004 SEC regulatory shock to mandatory portfolio disclosure frequency provides quasi-causal evidence that horizon-shortening causally reduces ESG tilts.

Core results

Magnitudes and significance are as reported; */**/*** = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Long-horizon mutual funds (lowest-turnover quintile) hold portfolios with meaningfully higher ESG scores than short-horizon funds (highest-turnover quintile)	Figure 1, Panel A, p. 614	Weighted-average ESG score of 0.91 (long-horizon quintile, turnover = 20%) vs. 0.42 (short-horizon quintile, turnover = 161%); spread = 28% of a standard deviation
R2	Same ESG-horizon gradient holds for 13f institutions sorted by churn ratio	Figure 1, Panel B, p. 614	ESG score of 1.55 (long-horizon, churn ratio = 10%) vs. 0.44 (short-horizon, churn ratio = 92%), monotonically decreasing
R3	Investor-level panel regression: fund turnover ratio is significantly negatively associated with portfolio ESG score, controlling for investment style and fund characteristics	Table II, col. (1), p. 616	Coeff. on Fund Turnover Ratio = -0.0968*** (s.e. 0.0197); 1-SD increase in turnover (0.49) corresponds to a 0.05-point decrease in fund ESG score
R4	Result is robust to churn ratio, adjusted churn ratio, and 13f institutions; 2SLS using alternative ESG scores as instruments (errors-in-variables approach) leaves results unchanged	Table II, cols. (2)-(5), p. 616; Table VI, p. 625	Fund Churn Ratio coeff. = -0.216*** (s.e. 0.0402); Adjusted Churn Ratio = -0.555*** (s.e. 0.0962); 13f Churn Ratio = -0.321*** (s.e. 0.0310)
R5	Firm-level: one-SD increase in MSCI ESG score (2.32) is associated with a decrease of 0.51 percentage points in weighted-average mutual fund shareholder turnover ratio	Table IV, col. (1), p. 621	MSCI ESG Score coeff. = -0.222** (s.e. 0.102); results hold for 13f churn ratio and transient-investor share
R6	Information channel: long-term mutual funds (low churn) are less likely to sell following a negative earnings surprise (patience), but more likely to sell following a negative ES incident (responsiveness to ESG news)	Table VII, p. 627; Table VIII, p. 629	Earnings shortfall: interaction coeff. (LongTermInvestor x NegEarningsSurprise) opposite sign to main effect. ES incident: long-term fund D(sell) interaction = 4.652*** (s.e. 3.699), D(liq) = 3.244*** (s.e. 1.348) percentage points higher probability
R7	Limits-to-arbitrage channel: shorter-horizon funds have higher flow-performance sensitivity (FPS); high FPS is negatively and significantly associated with fund portfolio ESG score	Table IX, cols. (1)-(3), p. 631	Turnover Ratio coeff. on FPS = 1.125*** (s.e. 0.294); FPS coeff. on Portfolio ESG = -0.337*** (s.e. 0.0875)
R8	Causal test (SEC 2004 DiD + 2SLS): funds required to switch from semiannual to quarterly portfolio disclosure shortened their horizon and subsequently reduced ESG portfolio tilts	Table X, p. 633	DiD reduced-form: TreatedFunds x PostDisclosure coeff. = -0.0430** (s.e. 0.0185); 2SLS second stage: Fund Turnover Ratio coeff. on Fund ESG = -0.683** (s.e. 0.333)
R9	Clientele-catering channel: no evidence that long-term fund investors respond differentially to Morningstar sustainability globe ratings, and ESG gap does not widen during periods of high climate news attention	Table XI, p. 636; Table XII, p. 638	Interaction (LongTermInvestor x Globe x Post) coefficients are statistically indistinguishable from zero; Horizon x CCNI interaction is near zero and insignificant in most specifications

Overall (paper’s conclusion). Long-horizon investors consistently prefer firms with better ESG profiles, supporting the view that investors have heterogeneous preferences regarding corporate ESG profiles and that this heterogeneity depends on investor horizons, related to work linking investor horizon to corporate policies (Derrien, Kecskés, and Thesmar 2013). The information channel and limits-to-arbitrage channel both receive empirical support; the clientele-catering channel does not.

Theory / model

The paper has no structural model. It tests three theoretical mechanisms from prior literature:

Information channel (Froot, Perold, and Stein 1992; Van Nieuwerburgh and Veldkamp 2009). Long-term investors specialize in collecting and interpreting long-payoff information such as ESG profiles; short-term investors specialize in short-frequency signals such as quarterly earnings. In equilibrium, long-term investors overweight firms with ESG-related projects because of an information advantage. The testable implication is that long-term funds are patient toward negative earnings surprises but reactive to ES incidents (the two carry different horizon implications).

Limits-to-arbitrage channel (Stein 2005; Giannetti and Kahraman 2018; Pedersen, Fitzgibbons, and Pomorski 2021). If ESG investing is a long-horizon arbitrage opportunity, open-end fund managers with high flow-performance sensitivity (FPS) are deterred from maintaining ESG tilts because short-run underperformance triggers outflows before the mispricing corrects. Low-FPS (long-horizon) funds face weaker redemption risk and can hold ESG positions longer. The testable implication is that FPS negatively mediates the horizon-ESG relationship.

Clientele-catering channel (Heinkel, Kraus, and Zechner 2001; Pastor, Stambaugh, and Taylor 2021). If end-investors of long-term funds have stronger nonpecuniary ESG preferences, managers cater by tilting toward high-ESG stocks. The testable implication is that ESG preferences of end-investors of long-term funds differ from those of short-term funds (tested via Morningstar globe flow responses and the CCNI interaction). The globe DiD design follows Hartzmark and Sussman (2019).

Identification. The primary cross-sectional tests control for investment objective by time, fund size, portfolio characteristics, and past return rank with two-way clustering of standard errors (fund and quarter). The key quasi-causal test (R8) exploits the 2004 SEC rule requiring mutual funds to disclose holdings quarterly rather than semiannually, using treated funds (those newly required to switch frequency) vs. control funds (those already disclosing quarterly) in both a DiD and a 2SLS framework where the regulatory interaction instruments for observed investment horizon.

Method

All results are produced by panel regressions and event-study-style portfolio comparisons. No new estimator is proposed. The main workhorse estimators are:

• panel-regression with investment objective-by-quarter or industry-by-year fixed effects and two-way clustered standard errors (fund and quarter, or stock and year).
• portfolio-sort into turnover or churn quintiles for the bivariate ESG comparisons (Figure 1).
• instrumental-variables (2SLS) with two uses: (a) using Refinitiv and Sustainalytics ESG scores as instruments for MSCI ESG scores to address errors-in-variables noise in ESG ratings (Tables V-VI, following Berg et al. 2022); and (b) using the TreatedFunds x PostDisclosure interaction as an instrument for investor horizon to isolate the causal effect of horizon on ESG tilts (Table X).
• difference-in-differences around the 2004 SEC portfolio disclosure rule (eq. 13) and around the 2016 Morningstar sustainability globe introduction (eq. 16), including fund fixed effects for within-fund identification.

The churn ratio $\text{CR}$ is constructed at the quarterly level, using the four-quarter moving average (Gaspar, Massa, and Matos 2005), and the adjusted churn ratio $\text{CR\_Adj}$ mitigates flow bias by taking the minimum of buy-side and sell-side churn (Yan and Zhang 2009). Both measures are used interchangeably throughout.

Empirical specifications

Investor-level ESG regression (R3, R4), eq. 7, p. 615:

$$\text{FundESG}_{j,t+1} = \alpha_t + \beta_1 \cdot \text{Horizon}_{j,t} + \beta_2 \cdot \text{Controls}_{j,t} + \epsilon_{j,t} \tag{7}$$

• $\text{FundESG}_{j,t+1}$ is the value-weighted average MSCI ESG score of fund $j$‘s portfolio holdings at the end of the following quarter.
• $\text{Horizon}_{j,t}$ is the fund’s annual turnover ratio, churn ratio, or adjusted churn ratio (four-quarter moving average).
• Controls include ln(Fund TNA), number of portfolio holdings, value-weighted portfolio market cap and book-to-market, past 12-month return, and fractional return rank.
• Sample: all fund-quarter observations. Fixed effects: investment objective-by-quarter. Standard errors: two-way clustered at the fund and quarter level.

Firm-level horizon regression (R5), eq. 8, p. 619:

$$\text{InvestorHorizon}_{i,t+1} = \alpha_t + \beta_1 \cdot \text{ESG}_{i,t} + \beta_2 \cdot \text{Controls}_{i,t} + \epsilon_{i,t} \tag{8}$$

• $\text{InvestorHorizon}_{i,t+1}$ is the weighted-average turnover ratio or churn ratio of firm $i$‘s mutual fund or 13f shareholders.
• $\text{ESG}_{i,t}$ is the MSCI ESG score.
• Controls include ln(market cap), book-to-market, dividend yield, profitability, past return, return volatility, stock turnover, and fund flow volatility.
• Fixed effects: industry (two-digit SIC) and year. Standard errors: double-clustered at stock and year.

Earnings-surprise trading regression (R6, information channel), eq. 9, p. 626:

$$\text{Dummy(Sell)}_{i,j,t} = \alpha_{j,t} + \beta_1 \cdot \text{EarningsShortfall}_{i,t} + \beta_2 \cdot \text{LongTermInvestor}_{j,t} \times \text{EarningsShortfall}_{i,t} + \text{Controls}_{j,t} + \epsilon_{i,j,t} \tag{9}$$

• $\text{Dummy(Sell)}_{i,j,t}$ is an indicator for fund $j$ selling stock $i$ in quarter $t$.
• $\text{EarningsShortfall}_{i,t}$ is a negative earnings surprise measure for stock $i$.
• $\text{LongTermInvestor}_{j,t}$ is an indicator for funds whose four-quarter trailing churn ratio falls below the 30th percentile.
• Sample: interquarter changes for positions held in previous quarter. Fixed effects: fund-by-quarter. Standard errors: double-clustered at the fund and quarter level.

ES-incident trading regression (R6, information channel), eq. 10, p. 628:

$$\text{Dummy(Sell)}_{i,j,t} = \alpha_{j,t} + \beta_1 \cdot \text{ESIncident}_{i,t-1} + \beta_2 \cdot \text{LongTermInvestor}_{j,t} \times \text{ESIncident}_{i,t-1} + \text{Controls}_{j,t} + \epsilon_{i,j,t} \tag{10}$$

• $\text{ESIncident}_{i,t-1}$ is an indicator from RepRisk for severe negative environmental or social incidents in the previous quarter.
• $\text{LongTermInvestor}_{j,t}$ is an indicator for low-churn funds (below 30th percentile trailing churn ratio).
• Specifications include both fund-by-quarter and stock-by-quarter fixed effects (columns 4-6 of Table VIII absorb all stock-time variation, isolating differential fund responses).

Flow-performance sensitivity and ESG (R7, limits-to-arbitrage), eqs. 11-12, p. 630:

$$\text{FPS}_{j,t} = \alpha_t + \beta_1 \cdot \text{Horizon}_{j,t} + \beta_2 \cdot \text{Controls}_{j,t} + \epsilon_{j,t} \tag{11}$$ $$\text{FundESG}_{j,t+1} = \alpha_t + \beta_1 \cdot \text{FPS}_{j,t} + \beta_2 \cdot \text{Controls}_{j,t} + \epsilon_{j,t} \tag{12}$$

• $\text{FPS}_{j,t}$ is the flow-performance sensitivity of fund $j$, estimated from a 24-month rolling OLS of monthly net fund flows on past 12-month average monthly return.
• Fixed effects: investment objective-by-quarter. Standard errors match the investor-level specification.

SEC 2004 DiD and 2SLS (R8, causal test), eqs. 13-15, pp. 632-633:

$$\text{FundESG}_{j,t+1} = \alpha_j + \gamma_t + \beta \cdot \text{TreatedFunds}_j \times \text{PostDisclosure}_t + \text{Controls} + \epsilon_{j,t} \tag{13}$$

• Reduced-form DiD. Treated funds switched from semiannual to quarterly disclosure; control funds were already disclosing quarterly. The SEC 2004 mandatory quarterly disclosure rule is used as an exogenous shock to fund horizon following Agarwal et al. (2015).
• Sample: 2001Q1-2008Q4. Fixed effects: fund ($\alpha_j$) and quarter ($\gamma_t$). Standard errors: clustered at the fund level.

$$\text{Horizon}_{j,t} = \alpha_j + \gamma_t + \beta \cdot \text{TreatedFunds}_j \times \text{PostDisclosure}_t + \text{Controls} + \epsilon_{j,t} \tag{14}$$ $$\text{FundESG}_{j,t+1} = \alpha_j + \gamma_t + \beta_1 \cdot \widehat{\text{Horizon}}_{j,t} + \beta_2 \cdot \text{Controls} + \epsilon_{j,t} \tag{15}$$

• Eq. 14 is the first stage of 2SLS; eq. 15 is the second stage, with $\widehat{\text{Horizon}}_{j,t}$ the fitted value from eq. 14. Standard errors: clustered at the fund level for serial dependence.

Datasets used

Dataset	Role in paper	Wiki page
CRSP Mutual Fund Database	Fund characteristics (TNA, turnover ratio, returns, flows, expense ratios); mutual fund sample construction (98,252 fund-years)	WRDS / CRSP (licensed)
Thomson Reuters s12 (mutual fund holdings)	Quarterly equity holdings for mutual funds; portfolio ESG construction	WRDS (licensed)
Thomson Reuters s34 (13f institutions)	Quarterly equity holdings for 13f institutions; 166,185 institution-year observations	WRDS (licensed)
MSCI ESG STATs (formerly KLD)	Annual positive/negative ESG indicators for firm-years; primary ESG scoring; 26,217 firm-years	KLD / MSCI ESG (licensed)
Refinitiv ESG (formerly ASSET4)	Alternative ESG scores for robustness (2009-2017 subsample); used as instrument in 2SLS	no page yet
Sustainalytics ESG	Alternative ESG scores for robustness (2009-2017); used as instrument in 2SLS	no page yet
Compustat annual fundamentals	Book-to-market, profitability, dividend yield; firm-level controls	WRDS / Compustat (licensed)
CRSP daily/monthly stock data	Market capitalization, stock turnover, past returns, return volatility	WRDS / CRSP (licensed)
I/B/E/S	Analyst earnings forecasts; second measure of earnings surprise for Table VII	I/B/E/S (licensed)
RepRisk	Negative environmental and social (ES) incident data; used in ES-incident trading tests (Table VIII)	RepRisk (licensed)
SEC EDGAR (N-CSR/N-CSRS filings)	Mutual fund shareholder reports; bag-of-words ESG mention analysis (Table III)	EDGAR
Bushee institutional investor classifications	Transient/dedicated/quasi-indexer classification from Bushee (1998); supplemental horizon measure for 13f institutions	no page yet
Climate Change News Index (CCNI)	Engle et al. (2020) index of WSJ climate reporting intensity; clientele-catering test (Table XII)	no page yet

Sample: 2000 to 2018 for main analyses; 2001-2008 for SEC 2004 DiD; 2015-2017 for Morningstar globe DiD. Quarterly frequency for fund/institution analyses; annual for firm-level.

When to read the full paper

Use the original if you are: studying institutional demand heterogeneity for ESG assets; designing tests of limits-to-arbitrage in ESG pricing; examining how regulatory shocks (disclosure frequency) affect fund portfolio composition; or extending the horizon-ESG nexus to non-US markets or private-asset investors. The locators above point to the exact tables and figures.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(2), April 2026. This distillation was extracted by an LLM on 2026-06-01 and is not human-verified or independently reproduced. The CC BY-NC-ND 4.0 licence permits non-commercial sharing with attribution but prohibits derivatives; the verbatim PDF is not hosted.

Attribution (CC BY-NC-ND 4.0). Starks, Laura T., Parth Venkat, and Qifei Zhu. “Corporate ESG Profiles and Investor Horizons.” The Journal of Finance 81, no. 2 (April 2026): 603-642. DOI: 10.1111/jofi.70008. (c) 2026 The Author(s). Licensed under CC BY-NC-ND 4.0. This page is a distilled summary by the Institute for Automated Research; it is not a reproduction of or derivative from the original article.